Structure the trade-off decision between AI models, providers, or approaches for a given product use case. Use this skill when a team needs to choose between AI options with different capability, cost, latency, and risk profiles.
name make-model-tradeoff-decision description Structure the trade-off decision between AI models, providers, or approaches for a given product use case. Use this skill when a team needs to choose between AI options with different capability, cost, latency, and risk profiles. Make Model Trade-off Decision Purpose Help teams make structured, explicit decisions about which AI model or approach to use for a product feature — balancing capability, cost, latency, risk, and user experience requirements. Skill type Conceptual skill Use this skill when Multiple model options are available and a choice must be made An existing model is underperforming and alternatives need evaluation A model upgrade is being considered and impact needs to be assessed Cost or latency constraints require trading off capability Do not use this skill when The goal is assessing a single model's capabilities (use assess-model-capabilities) The goal is setting up AI quality monitoring (use evaluate-ai-quality-monitoring) Required inputs Product use case requiring AI Candidate models or approaches to compare (at least 2) Key requirements: capability needs, latency budget, cost constraints Optional inputs Quality evaluation data from initial testing User tolerance for latency and errors Regulatory or data privacy constraints Build vs. buy vs. fine-tune context Upstream context Works best when: AI feature value is framed Capability requirements are defined Downstream handoff Output can feed: design-human-in-loop-workflow (model choice affects oversight needs) evaluate-ai-quality-monitoring (selected model → quality criteria) write-requirements-prd (decision → technical requirements) Instructions Define the capability requirements for the use case (accuracy, format, language support, etc.). Define the non-functional requirements: latency, cost per call, data privacy. Identify candidate models or approaches. Evaluate each candidate against requirements. Identify the key trade-offs (capability vs. cost, capability vs. latency, control vs. convenience). Recommend the best option for the use case with explicit trade-off rationale. Define when the decision should be re-evaluated. Output Provide: Use case requirements (functional and non-functional) Candidate models/approaches Evaluation matrix Key trade-offs identified Recommendation with rationale What the chosen option sacrifices Re-evaluation triggers Risks / caveats Model benchmarks don't always predict real-world performance on your specific use case — test on real data Cost at current scale may look trivial but must be modeled at target scale Model vendor lock-in is a real risk — consider abstraction layers for high-stakes decisions